Quantified Self

Abstract: The last several years have seen an explosion of interest in wearable computing, personal tracking devices, and the so-called quantified self (QS) movement. Quantified self involves ordinary people recording and analyzing numerous aspects of their lives to understand and improve themselves. This is now a mainstream phenomenon, attracting a great deal of attention, participation, and funding. As more people are attracted to the movement, companies are offering various new platforms (hardware and software) that allow ever more aspects of daily life to be tracked. Nearly every aspect of the QS ecosystem is advancing rapidly, except for analytic capabilities, which remain surprisingly primitive. With increasing numbers of qualified self participants collecting ever greater amounts and types of data, many people literally have more data than they know what to do with. This article reviews the opportunities and challenges posed by the QS movement. Data science provides well-tested techniques for knowle...

Abstract: Wearable sensors are an integral part of the new telemedicine concept supporting the idea that Information Technologies will improve the quality and efficiency of healthcare. The use of sensors in diagnosis, treatment and monitoring of patients not only potentially changes medical practice but also one's relationship with one's body and mind, as well as the role and responsibilities of patients and healthcare professionals. In this paper, we focus on knowledge assessment of the online communities of Fitbit (a commercial wearable device) and the Quantified Self movement. Through their online forums, we investigate how users' knowledge claims, shared experiences and imaginations about wearable sensors interrogate or confirm the narratives through which they are introduced to the publics. Citizen initiatives like the Quantified Self movement claim the right to 'own' the sensor generated data. But how these data can be used through traditional healthcare systems is an open question. More importantly, wearable sensors trigger a social function that is transformative of the current idea of care and healthcare, focused on sharing, socialising and collectively reflecting about individual problems. Whether this is aligned with current policy making about healthcare, whose central narrative is focused on efficiency and productivity, is to be seen.

Abstract: Wearable devices with a wide range of sensors have contributed to the rise of the Quantified Self movement, where individuals log everything ranging from the number of steps they have taken, to their heart rate, to their sleeping patterns. Sensors do not, however, typically sense the social and ambient environment of the users, such as general life style attributes or information about their social network. This means that the users themselves, and the medical practitioners, privy to the wearable sensor data, only have a narrow view of the individual, limited mainly to certain aspects of their physical condition. In this paper we describe a number of use cases for how social media can be used to complement the check-up data and those from sensors to gain a more holistic view on individuals' health, a perspective we call the 360° Quantified Self. Health-related information can be obtained from sources as diverse as food photo sharing, location check-ins, or profile pictures. Additionally, information from a person's ego network can shed light on the social dimension of wellbeing which is widely acknowledged to be of utmost importance, even though they are currently rarely used for medical diagnosis. We articulate a long-term vision describing the desirable list of technical advances and variety of data to achieve an integrated system encompassing Electronic Health Records (EHR), data from wearable devices, alongside information derived from social media data.

Abstract: A growing number of studies use wearable sensors, including cameras, to detect user activity patterns. When an object of academic investigation, these patterns are interpreted by researchers and conclusions are drawn about people's habits and routines. Alternatively, interpretations are provided by users themselves during extensive post-study interviews. Such approaches inevitably expose personal data collected about individuals to researchers, which can potentially change the behavior under investigation. We introduce a new approach to using wearable sensor data in research. It allows people to interpret and self-reflect on their data and submit for investigation only reflections, without sharing their raw data. In this interactivity, we present and discuss the Datawear mobile application prototype, which is designed to conduct "in the wild" studies of personal experiences.